Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group

Our plain-English summary of Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group, by S.G. Armato, R.J. Francis, S.I. Katz, et al (Lung Cancer, 2019).

The study

• Authors: S.G. Armato, R.J. Francis, S.I. Katz, G. Ak, I. Opitz, E. Gudmundsson, K.G. Blyth, A. Gupta
• Journal: Lung Cancer
• Published: 2019;130:108-114
• Type of article: Review
• Cited by: 15 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Mesothelioma patients rely on the information their clinical team obtains from medical imaging. Whether x-ray-based computed tomography (CT) or magnetic resonance imaging (MRI) based on local magnetic fields within a patient's tissues, different modalities generate images with uniquely different appearances and information content due to the physical differences of the image-acquisition process. Researchers are developing sophisticated ways to extract a greater amount of the information contained within these images. This paper summarizes the imaging-based research presented orally at the 2018 International Conference of the International Mesothelioma Interest Group (iMig) in Ottawa, Ontario, Canada, held May 2-5, 2018. Presented topics included advances in the imaging of preclinical mesothelioma models to inform clinical therapeutic strategies, optimization of the time delay between contrast administration and image acquisition for maximized enhancement of mesothelioma tumor on CT, an investigation of image-based criteria for clinical tumor and nodal staging of mesothelioma by contrast-enhanced CT, an investigation of methods for the extraction of mesothelioma tumor volume from MRI and the association of volume with patient survival, the use of deep learning for mesothelioma tumor segmentation in CT, and an evaluation of CT-based radiomics for the prognosis of mesothelioma patient survival.

Copyright © 2018 Elsevier B.V. All rights reserved.

In plain English

This article was a review, not a single study. The authors summarized pleural mesothelioma imaging talks from the 2018 International Mesothelioma Interest Group conference. One talk described PET imaging of mouse models. In 10 patients, the estimated optimal CT delay after contrast injection was 230 to 300 seconds, when over 95% of maximal tumor enhancement was reached in all patients. The usual chest CT delay is 40 to 60 seconds. In a CT review of 210 patients, 14 with distant metastasis were excluded. Among the other 196, rind-type tumor (median survival 11.2 months, 95% CI 8.9 to 13.3) and spread to lymph nodes both within and outside the lung cancer node map (median 5.4 months, 95% CI 5 to 5.8) were linked with poorer survival. In 30 patients, two CT radiomic features predicted survival better than tumor volume. A deep learning method's CT tumor outlines overlapped radiologists' outlines significantly more than a 2011 method's. In 31 patients, higher MRI tumor volume was independently linked with shorter survival (multivariable hazard ratio 2.114, interval 1.046 to 4.270). The authors called several studies small or pilot work needing validation.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Review summarizing imaging research presented orally at a conference (2018 iMig conference). It covers several separate presentations: preclinical (mouse) imaging, a small prospective CT trial, a retrospective CT staging review, a CT radiomics study, a deep learning segmentation study and an MRI volumetry pilot study.
• Who was studied: Different groups for each presentation, all with malignant pleural mesothelioma (MPM) except the mouse work. The CT timing trial enrolled 10 adult MPM patients planned for pleurectomy. The staging review looked at pretreatment CT scans of 210 MPM patients diagnosed and treated between 2010 and 2016. Of these, 14 with distant metastasis were excluded, leaving 196. The radiomics study included 30 MPM patients. For deep learning, the networks were trained on 146 CT scans from 87 MPM patients and tested on 63 CT sections from 17 other patients. The MRI study included 31 MPM patients from Glasgow centres in the UK DIAPHRAGM study. Preclinical work used mouse models, including the MexTAg mouse.
• Exposure: Not an exposure study. The presentations evaluated imaging methods (CT, MRI, PET, radiomics, deep learning) in mesothelioma. Asbestos exposure of patients and fiber types are not reported. Asbestos is mentioned only in describing the MexTAg mouse model, citing other work.
• Main results:
  • In the 10-patient CT trial, best-fit analysis estimated an optimal delay of 230 to 300 seconds after IV contrast. In that range, more than 95% of maximal tumor enhancement was achieved for all 10 patients. This is later than the conventional 40 to 60 seconds for chest CT.
  • In the CT staging review (after exclusions), median survival for all patients was 14.2 months (95% CI: 10.6 to 17.8 months), with a 2-year survival of 31%.
  • In the CT staging review, median survival by histology was 20.1 months (epithelioid), 10.5 months (biphasic) and 7 months (sarcomatoid). The difference was significant (p less than 0.001).
  • In the CT staging review, median survival by tumor appearance was 20.9 months for fluid only and localized tumor combined, 14.6 months for heterogeneous diffuse disease, and 11.2 months for homogenous or heterogeneous rinds. Rinds had the worst survival (p=0.031; 95% CI: 8.9 to 13.3 months).
  • In the CT staging review, median survival was 17.3 months for lymph node metastasis within the lung cancer lymph node map, 11.7 months for metastasis outside it, and 5.4 months for metastasis both within and outside it (p=0.003; 95% CI: 5 to 5.8 months). Any pleural thickening was associated with poor survival. The location of the thickest part of the tumor was not associated with survival.
  • In the 30-patient radiomics study, 505 of 1404 radiomic features were stable. Two wavelet features were prognostic for overall survival in multivariable analysis (concordance index 0.74, p=0.002). Tumor volume was prognostic in univariate analysis (p=0.01) but had a smaller concordance index of 0.62.
  • In the deep learning study, overlap with the three observers' reference tumor outlines was measured by the median Dice similarity coefficient. It was 0.776, 0.688 and 0.800 for the deep learning method, compared with 0.718, 0.604 and 0.709 for the 2011 Method. The greater overlap for the deep learning method was significant for all observers (p less than 0.001).
  • In the 31-patient MRI study, patients with higher tumor volumes (300 ml or more) had significantly poorer median overall survival. Rising tumor volume by tertile was associated with falling median survival (p=0.023). In a multivariable model, tumor volume was independently associated with overall survival (HR: 2.114, reported interval 1.046 to 4.270, p=0.037).
• The authors' conclusion: The authors concluded that radiomics, as a potential prognostic biomarker, and deep convolutional neural networks, for automated tumor segmentation, had opened a promising new area of mesothelioma research in CT. They also concluded that the link between tumor volume and survival, and methods to measure volume more accurately, remain topics of interest. In the individual sections, they stated that rind-type thickening and combined nodal spread should be considered in future staging efforts.
• Limitations the authors state:
  • The CT contrast timing study was a small prospective trial. The authors called for further study, ideally in a multicenter prospective trial.
  • In the staging review, the authors said tumor thickness needs evaluation in a larger series.
  • The radiomics results had not yet been validated in a larger cohort.
  • The deep learning method needs training on a larger set of annotated CT scans to improve its accuracy and robustness.
  • The MRI volume work was a pilot study, and validation against resected tumor volumes was still planned.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Mesothelioma.

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Mesothelioma, IMIG, Staging, Prognosis.

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• Imaging in pleural mesothelioma: A review of the 13th International Conference of the International Mesothelioma Interest Group — S.G. Armato, K.G. Blyth, et al. (2016)
• Molecular pathways and diagnosis in malignant mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group — D.B. Chapel, A. Churg, et al. (2019)
• Imaging in pleural mesothelioma: a review of imaging research presented at the 9th International Meeting of the International Mesothelioma Interest Group — A.K. Nowak, S.G. Armato, et al. (2010)
• Progress of malignant mesothelioma research in basic science: A review of the 14th international conference of the international mesothelioma interest group (iMig2018) — L. Wu, I. Dell'Anno, et al. (2019)
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